PROJECT SUMMARY Neuroendocrine tumors (NETs) are one heterogeneous type of cancer affecting most organ systems. NETs must be correctly graded to ensure proper treatment and patient management. The proliferation index, as measured by Ki67 nuclear staining, is required for gastrointestinal (GI) and pancreatic NET grading per the criteria of the World Health Organization (WHO). Measuring the Ki67 labeling index (Ki67 LI) from pathology images requires accurate quantification of immunopositive tumor, immunonegative tumor and non-tumor cells. This process is an essential procedure in basic, translational and clinical research and in routine clinical practice. However, current Ki67 image analysis tools have a number of drawbacks: 1) Ki67 LI assessment is still mainly achieved with manual or semi-automated methods, leading to increased labor costs, awkward workflows, low-throughput image analysis and significant potential inter- and intra-observer variability; 2) computer-aided Ki67 counting is error-prone due to the multi-stage image processing design, where each stage itself is a very challenging task; 3) current algorithm design does not take into consideration the characteristics of Ki67 images such that it has technical difficulty in classifying different types of cells in Ki67 stained images. In this proposed research, we seek to develop and disseminate a novel deep learning-based imaging informatics system, KiNeT, specifically for better automated Ki67 LI measurement in GI and pancreatic NETs. KiNet will take advantage of cutting-edge machine learning algorithms, deep fully convolutional networks (FCNs), to develop an end-to-end, pixel-to-pixel model for single-stage Ki67 LI assessment. To this end, we will first formulate Ki67 counting as a cell identification problem and solve it using class-aware structured regression modeling within a novel FCN network. This network will simultaneously detect and classify immunopositive tumor, immunonegative tumor and non-tumor cells. Next, we will further enhance cell identification with another related task, extraction of regions of interest (ROIs), which will differentiate tumor from non-tumor regions by taking Ki67 image characteristics into consideration. These two tasks will be unified into one single neural network and jointly learned to benefit both cell identification and region classification. KiNet will provide a novel computational method for accurate Ki67 LI assessment, thereby enabling early detection and targeted treatments of the diseases. Compared to manual counting and current Ki67 image analysis methods, it will significantly improve the objectivity, consistency, reliability, reproducibility and efficiency. Additionally, the proposed single-stage Ki67 counting strategy, which is completely different from current multi-stage Ki67 image analysis pipelines, will provide a new perspective for Ki67 image quantification.